Furuoka, Fumitaka and Yaya, OlaOluwa S and Ling, Piu Kiew and Al-Faryan, Mamdouh Abdulaziz Saleh and Islam, M. Nazmul (2023): Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management. Published in: Resources Policy
Preview |
PDF
MPRA_paper_117003.pdf Download (704kB) | Preview |
Abstract
This paper examines energy and agricultural commodities' short-run and long-run connectedness by using the Time-varying parameter vector autoregressions (TVP-VAR). It applies the frequency version of the TVP-VAR model, which is a modified version of the dynamic TVP-VAR model. The frequency decomposition definition also decomposes into short-run and long-run connectedness. We further the analysis by investigating the effect of asymmetry in returns on connectedness. It also examines how portfolio management strategies would lead to a maximization of profits with minimal risks. Empirical evidence indicates that only 32.52% and 31.38% of connectedness in oil and gas, respectively, are transmitted to agricultural commodities, which suggests their weak tendencies in influencing agricultural commodities; the total connectedness index hovers around 40-60% in the 2018-2019 period; however, it dropped below 40% in 2020-2021 when the COVID-19 pandemic contributed to disintegrate the connectedness between energy and agricultural commodities but increased further during the 2022 Russia-Ukraine saga. The findings also indicate that corn, wheat, and flour are net transmitters of risks to oil and natural gas in the long and short-run, and wheat-flour pairwise connectedness is the strongest in the connectedness. Asymmetry is also pronounced in the network of connectedness. Portfolio analyses indicate that investors require a low proportion of energy in a portfolio of energy-agricultural commodities to achieve an optimum profit. The findings will offer exciting insights into the connectedness of agricultural and energy commodities, particularly during periods of high price uncertainty.
Item Type: | MPRA Paper |
---|---|
Original Title: | Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management |
Language: | English |
Keywords: | Agricultural commodity; Asymmetry; Frequency TVP-VAR; Optimal weight; Risk |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 117003 |
Depositing User: | Dr OlaOluwa Yaya |
Date Deposited: | 10 Apr 2023 13:22 |
Last Modified: | 10 Apr 2023 13:22 |
References: | Adekoya, O.B. and Oliyide, J. (2022). The hedging effectiveness of industrial metals against different oil shocks: evidence from the four newly developed oil shocks datasets. Resources Policy, 69, December 2020, 101831. Adekoya, O. B., Akinseye, A. B., Antonakakis, N., Chatziantonviou, I., Gabauer, D. and Oliyide, J. (2022a). Crude oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies. Resources policy https://doi.org/10.1016/j.resourpol.2022.102877 Adekoya, O.B., Oliyide, J. A., Yaya, O. S. and Al-Faryan, M. A. S. (2022b). Does oil connect differently with prominent assets during war? Evidence from intra-day data during the Russia Ukraine saga. Resources Policy, Volume 77, 102728. Ajao, I. O., Olugbode, M. A., Olayinka, H. A., Yaya, O. S. and Shittu, O. I. (2022). Long Memory cointegration and Dynamic Connectedness of Volatility in US dollar Exchange rates, with FOREX portfolio investment strategy. SSRN paper. Amaglobeli, D., Hanedar, E., Hong, G. H. and Thevenot, C. (2022). Fiscal policy for mitigating the social impact of high energy and food prices, IMF Note 2022/001, International Monetary Fund, Washington, DC. Antonakakis, N., Gabauer, D., & Gupta, R. (2019). International monetary policy spillovers: evidence from a time-varying parameter vector autoregression. International Review of Financial Analysis, 65, 101382. Antonakakis, N., Chatziantoniou, I., and Gabauer, D. (2020a). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4):84. Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., and de Gracia, F. P. (2020b). Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness. Energy Economics, 91, 104762. Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., and De Gracia, F. P. (2018). Oil volatility, oil and gas firms and portfolio diversification. Energy Economics, 70, 499-515. Awartani, B., Maghyereh, A. I., and Al Shiab, M. (2013). Directional spillovers from the US and the Saudi market to equities in the Gulf Cooperation Council countries. Journal of International Financial Markets, Institutions and Money, 27, 224-242. Awartani, B., and Maghyereh, A. I. (2013). Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries. Energy Economics, 36, 28-42. Awe, O. O., Akinlana, D. M., Yaya, O. S. and Aromolaran, O. (2018). Time Series Analysis of the Behaviour of Import and Export of Agricultural and Non-Agricultural Goods in West Africa: A Case Study of Nigeria. Agris-Online Papers in Economics and Informatics, 10(2): 15-22. Baruník, J., and Krehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. Battistini, N., Grapow, H., Hahn, E. and Soudan, M. (2022). Wage share dynamics and second-round effects on inflation after energy price surges in the 1970s and today, ECB Economic Bulletin, Issue 5/2022, European Central Bank. Chatziantoniou, I., Gabauer, D., and Gupta, R. (2021). Integration and Risk Transmission in the Market for Crude Oil: A Time-Varying Parameter Frequency Connectedness Approach (No. 202147). Cronin, D. (2014). The interaction between money and asset markets: A spillover index approach. Journal of Macroeconomics, 39, 185-202. Dahlquist, M., Farago, A., and Tedongap, R. (2017). Asymmetries and portfolio choice. Review of Financial Studies, 30(2): 667–702. Diebold, F. X., and Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119(534), 158-171. Diebold, F. X., and Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66. Duncan, A. S., and Kabundi, A. (2013). Domestic and foreign sources of volatility spillover to South African asset classes. Economic Modelling, 31, 566-573. Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170. Elliott, G., Rothenberg, T. J., and Stock, J. H. (1996). Efficient Tests For An Autoregressive Unit Root. Econometrica, 64(4): 813–836. Fisher, T. J. and Gallagher, C. M. (2012). New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing. Journal of the American Statistical Association, 107 (498): 777–787. Gabauer, D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, 100680. Gil-Alana, L. A. and Yaya, O. S. (2014). The Relationship between Oil Prices and the Nigerian Stock Market: An Analysis based on Fractional integration and cointegration. Energy Economics, 46: 328-333. Gil-Alana, L. A., Gupta, R., Olubusoye, O. E. and Yaya, O. S. (2016). Time Series Analysis of Persistence in Crude Oil Price Volatility across Bull and Bear Regimes. Energy, 109: 29-37. Gong, X., and Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. Energy Economics, 110, 106028. Ji, Q., Bouri, E., Lau, C. K. M., and Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63, 257-272. Kroner, K. F. and Ng, V. K. (1998). Modelling Asymmetric Movements of Asset Prices. Review of Financial Studies 11(04), 817-844. Kroner, K. F. and Sultan, J. (1993). Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures. Journal of Financial and Quantitative Analysis 28(04), 535-551. Maghyereh, A. I., Awartani, B., and Bouri, E. (2016). The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes. Energy Economics, 57, 78-93. Malik, F., & Umar, Z. (2019). Dynamic connectedness of oil price shocks and exchange rates. Energy Economics, 84, 104501. Mo, B., Meng, J., and Zheng, L. (2022). Time and frequency dynamics of connectedness between cryptocurrencies and commodity markets. Resources Policy, 77, 102731. Pagan, A. and Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18, 23–46. Parker, M. (2017). Global inflation: The role of food, housing and energy prices, ECB Working Paper, No. 2024, ISBN 978-92-899-2746-8, European Central Bank (ECB), Frankfurt a. M., https://doi.org/10.2866/243933. Pesaran, H. H. and Shin, Y. (1998). Generalized Impulse Response Analysis In Linear Multivariate Models. Economics Letters, 58(1), 17–29. Taghizadeh-Hesary, F., Rasoulinezhad, E. and Yoshino, N. (2018). Volatility linkages between energy and food prices: Case of selected Asian countries, ADBI Working Paper Series No. 829, Asian Development Bank Institute. Tiwari, A. K, Abakah, E. J. A., Yaya O. S. and Appiah, K. O. (2022). Tail risk dependence, comovement and predictability between green bond and green stocks. Applied Economics. https://doi.org/10.1080/00036846.2022.2085869. World Bank, Commodity Market Outlook October 2022 (2022) International Bank for Reconstruction and Development. Yaya, O. S., Gil-Alana, L. A. and Shittu, O. I. (2015). Fractional Integration and Asymmetric Volatility in European, American and Asian Bull and Bear Markets: Application of High Frequency Stock Data. International Journal of Finance and Economics, 20(3): 276-290 Zhang, D. (2017). Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Economics, 62, 323-333. Zhang, B. and Wang, P. (2014). Return and volatility spillovers between China and world oil markets. Economic Modelling, 42, 413-420. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/117003 |